Abstract:

This research presents the implementation of maze-solving algorithms using artificial intelligence techniques through the Pi:Co Classic3 learning kit. The study emerges from the need to strengthen practical learning in technical institutes that lack advanced technological equipment, limiting students’ ability to develop competencies in robotics and applied AI. A mixed-method experimental design was used to evaluate the robot’s performance through physical tests and quantitative data analysis in a controlled environment. Three algorithms were integrated into the system: Adachi for autonomous exploration, Flood Fill for optimal path calculation, and A* as an external verification tool through a graphical interface. The results demonstrate that the Pi:Co Classic3 robot can independently explore and navigate mazes, optimize routes, and make decisions based on real-time sensor data. These findings highlight the kit’s potential as an effective educational resource and its relevance for teaching AI techniques in learning environments with limited technological resources.

Keywords:

Pi:co classic3, robotics, artificial intelligence, mazes, algorithms, technical education.